Adaptive Game Playing Using Multiplicative Weights
نویسندگان
چکیده
منابع مشابه
Adaptive Game Playing Using Multiplicative Weights
We present a simple algorithm for playing a repeated game. We show that a player using this algorithm suffers average loss that is guaranteed to come close to the minimum loss achievable by any fixed strategy. Our bounds are nonasymptotic and hold for any opponent. The algorithm, which uses the multiplicative-weight methods of Littlestone and Warmuth, is analyzed using the Kullback–Liebler dive...
متن کاملMultiplicative Weights
In this lecture, we will study various applications of the theory of Multiplicative Weights (MW). In this section, we briefly review the general version of the MW algorithm that we studied in the previous lecture. The following sections then show how the theory can be applied to approximately solve zero-sum games and linear programs, and how it connects with the theory of boosting and approxima...
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We saw that using the multiplicative weights (MW) algorithm, we find a (1 + ε)-approximate max flow f̂—i.e., a flow of value F that has f̂e ≤ 1 + ε—using O( logm ε2 ) calls to the oracle. In Lecture #14, we saw that using shortest-path routing, you can get ρ = F . Since we can use Dijkstra’s O(m+ n log n) to implement the oracle, this gives an Õ( ε2 ) time algorithm. Relaxed Oracle: For the rest ...
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In this lecture, first we propose an algorithm to solve semidefinite programs and then we will apply it to MAXCUT problem as an example. As you will see, we need an oracle with specific properties for our method to work, so we will show how to build such an oracle for MAXCUT problem. Finally, we investigate the quality of the SDP relaxation for a more general cases of discrete quadratic program...
متن کاملThe Multiplicative Weights Algorithm
Professors Greenwald and Oyakawa 2017-04-19 In these notes, we improve on the basic expert advice algorithms previously introduced. The basis for this technique is that we give each expert a weight, which influences how much sway she has in the algorithm’s decision. An expert who is wrong will frequently have her weight reduced and will therefore have less of an impact on the algorithm’s decisi...
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ژورنال
عنوان ژورنال: Games and Economic Behavior
سال: 1999
ISSN: 0899-8256
DOI: 10.1006/game.1999.0738